app.py

import gradio as gr
import torch
import numpy as np
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers import RobertaForSequenceClassification, RobertaTokenizer
from motif_tagging import detect_motifs
from abuse_type_mapping import determine_abuse_type

# custom fine-tuned sentiment model
sentiment_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-sentiment")
sentiment_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tether-sentiment")

# Load abuse pattern model
model_name = "SamanthaStorm/autotrain-c1un8-p8vzo"
model = RobertaForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)
tokenizer = RobertaTokenizer.from_pretrained(model_name, trust_remote_code=True)

LABELS = [
    "gaslighting", "mockery", "dismissiveness", "control", "guilt_tripping", "apology_baiting", "blame_shifting", "projection",
    "contradictory_statements", "manipulation", "deflection", "insults", "obscure_formal", "recovery_phase", "non_abusive",
    "suicidal_threat", "physical_threat", "extreme_control"
]

THRESHOLDS = {
    "gaslighting": 0.25, "mockery": 0.15, "dismissiveness": 0.45, "control": 0.43, "guilt_tripping": 0.15,
    "apology_baiting": 0.2, "blame_shifting": 0.23, "projection": 0.50, "contradictory_statements": 0.25,
    "manipulation": 0.25, "deflection": 0.30, "insults": 0.34, "obscure_formal": 0.25, "recovery_phase": 0.25,
    "non_abusive": 2.0, "suicidal_threat": 0.45, "physical_threat": 0.02, "extreme_control": 0.30
}

PATTERN_LABELS = LABELS[:15]
DANGER_LABELS = LABELS[15:18]

EXPLANATIONS = {
    "gaslighting": "Gaslighting involves making someone question their own reality or perceptions...",
    "blame_shifting": "Blame-shifting is when one person redirects the responsibility...",
    "projection": "Projection involves accusing the victim of behaviors the abuser exhibits.",
    "dismissiveness": "Dismissiveness is belittling or disregarding another person’s feelings.",
    "mockery": "Mockery ridicules someone in a hurtful, humiliating way.",
    "recovery_phase": "Recovery phase dismisses someone's emotional healing process.",
    "insults": "Insults are derogatory remarks aimed at degrading someone.",
    "apology_baiting": "Apology-baiting manipulates victims into apologizing for abuser's behavior.",
    "deflection": "Deflection avoids accountability by redirecting blame.",
    "control": "Control restricts autonomy through manipulation or coercion.",
    "extreme_control": "Extreme control dominates decisions and behaviors entirely.",
    "physical_threat": "Physical threats signal risk of bodily harm.",
    "suicidal_threat": "Suicidal threats manipulate others using self-harm threats.",
    "guilt_tripping": "Guilt-tripping uses guilt to manipulate someone’s actions.",
    "manipulation": "Manipulation deceives to influence or control outcomes.",
    "non_abusive": "Non-abusive language is respectful and free of coercion.",
    "obscure_formal": "Obscure/formal language manipulates through confusion or superiority."
}

PATTERN_WEIGHTS = {
    "physical_threat": 1.5,
    "suicidal_threat": 1.4,
    "extreme_control": 1.5,
    "gaslighting": 1.3,
    "control": 1.2,
    "dismissiveness": 0.8,
    "non_abusive": 0.0
}

def custom_sentiment(text):
    inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = sentiment_model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
        label_idx = torch.argmax(probs).item()
    label_map = {0: "supportive", 1: "undermining"}
    label = label_map[label_idx]
    score = probs[0][label_idx].item()
    return {"label": label, "score": score}

def calculate_abuse_level(scores, thresholds, motif_hits=None):
    weighted_scores = []
    for label, score in zip(LABELS, scores):
        if score > thresholds[label]:
            weight = PATTERN_WEIGHTS.get(label, 1.0)
            weighted_scores.append(score * weight)
    base_score = round(np.mean(weighted_scores) * 100, 2) if weighted_scores else 0.0
    motif_hits = motif_hits or []
    if any(label in motif_hits for label in {"physical_threat", "suicidal_threat", "extreme_control"}):
        base_score = max(base_score, 75.0)
    return base_score

def interpret_abuse_level(score):
    if score > 80:
        return "Extreme / High Risk"
    elif score > 60:
        return "Severe / Harmful Pattern Present"
    elif score > 40:
        return "Likely Abuse"
    elif score > 20:
        return "Mild Concern"
    return "Very Low / Likely Safe"

def analyze_single_message(text, contextual_flags):
    motif_flags, matched_phrases = detect_motifs(text)
    risk_flags = list(set(contextual_flags + motif_flags)) if contextual_flags else motif_flags
    sentiment_result = custom_sentiment(text)
    sentiment_label = sentiment_result["label"]
    sentiment_score = sentiment_result["score"]
    thresholds = {k: v * 0.8 for k, v in THRESHOLDS.items()} if sentiment_label == "undermining" else THRESHOLDS.copy()
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
    threshold_labels = [label for label, score in zip(PATTERN_LABELS, scores[:15]) if score > thresholds[label]]
    phrase_labels = [label for label, _ in matched_phrases]
    pattern_labels_used = list(set(threshold_labels + phrase_labels))
    abuse_level = calculate_abuse_level(scores, thresholds, motif_hits=[label for label, _ in matched_phrases])
    abuse_description = interpret_abuse_level(abuse_level)
    return {
        "text": text,
        "score": abuse_level,
        "summary": abuse_description,
        "sentiment": f"{sentiment_label} ({sentiment_score*100:.2f}%)",
        "top_labels": pattern_labels_used[:2],
        "matched_phrases": matched_phrases,
        "flags": contextual_flags
    }

def analyze_composite(msg1, msg2, msg3, flags):
    results = [analyze_single_message(t, flags) for t in [msg1, msg2, msg3] if t.strip()]
    composite_score = round(np.mean([r['score'] for r in results]), 2) if results else 0.0
    return [
        f"Score: {r['score']}% – {r['summary']}\nSentiment: {r['sentiment']}\nFlags: {', '.join(r['flags']) if r['flags'] else 'None'}\nLabels: {', '.join(r['top_labels'])}" for r in results
    ] + [f"Composite Abuse Score: {composite_score}%"]

iface = gr.Interface(
    fn=analyze_composite,
    inputs=[
        gr.Textbox(label="Message 1"),
        gr.Textbox(label="Message 2"),
        gr.Textbox(label="Message 3"),
        gr.CheckboxGroup(label="Contextual Flags", choices=[
            "They've threatened harm", "They isolate me", "I’ve changed my behavior out of fear",
            "They monitor/follow me", "I feel unsafe when alone with them"
        ])
    ],
    outputs=[
        gr.Textbox(label="Message 1 Result"),
        gr.Textbox(label="Message 2 Result"),
        gr.Textbox(label="Message 3 Result"),
        gr.Textbox(label="Composite Score")
    ],
    title="Abuse Pattern Detector (Multi-Message)",
    flagging_mode="manual"
)

if __name__ == "__main__":
    iface.queue().launch()